Apache Airflow

Apache Airflow is the most widely used open-source platform for orchestrating data workflows. You define pipelines as Python code — DAGs, or directed acyclic graphs — and Airflow schedules them, runs the tasks in the right order, retries failures, and gives you a web interface to monitor everything. It is the standard scheduler for data engineering teams.

Key Features

• Free open-source workflow orchestration
• Pipelines defined as Python code (DAGs)
• Scheduling, retries, and monitoring built in
• Hundreds of operators for databases and cloud
• Web UI for monitoring and managing runs
• Runs anywhere, including Kubernetes

Pricing

Prices as listed in 2026 research. Always check the official site for the latest price — plans change. Free plan: Yes — it has a free plan.

How it works

Airflow's strength is its flexibility and ecosystem. It has hundreds of operators and providers for databases, cloud services, and data tools, and because pipelines are code, they are versioned, tested, and reviewed like software. It runs anywhere — on your own servers, in Kubernetes, or as a managed service such as Astronomer Astro or a cloud provider's offering.

Apache Airflow is completely free as open-source software — you only pay for the infrastructure you run it on. It is essential for data teams that need reliable, code-based orchestration, though it takes engineering effort to set up and maintain, and it is not a tool for business users.

Pros and Cons

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